AI Enterprise Architect- AD

Job highlights

• . Bachelors degree in Computer Science,Engineering,or a related field
• Experience with web application development technologies and frameworks (frontend and / or backend)
• Experience with API design and management
• or equivalent practical experience
• . 14-18 years of experience in solution architecture,with a strong focus on application development

Key Responsibilities:

• Design and architect end-to-end solutions for complex business problems, considering scalability, performance, security, and cost-effectiveness.

• Lead the design and implementation of microservices-based architectures, defining service boundaries, APIs, and interaction patterns.

• Architect and integrate Generative AI and Machine Learning models into applications, defining data pipelines, model deployment strategies, and inference mechanisms.

• Collaborate with data scientists, ML engineers, and development teams to translate models into production-ready, scalable solutions.

• Define and enforce architectural standards, patterns, and best practices across application development teams.

• Evaluate and select appropriate technologies, frameworks, and tools for application development, microservices, AI/ML integration, and cloud deployment.

• Design and optimize solutions for deployment on at least one major cloud platform (AWS, Azure, or GCP), leveraging relevant cloud services (e.g., compute, storage, databases, AI/ML services, networking).

• Provide technical leadership and guidance to development teams throughout the software development lifecycle.

• Create and maintain technical documentation, including architectural diagrams, design specifications, and technical guidelines.

• Collaborate with stakeholders, including product managers, business analysts, and other architects, to understand requirements and translate them into technical designs.

• Stay abreast of the latest trends and advancements in microservices, GenAI, Machine Learning, cloud computing, and web technologies.

• Drive innovation by identifying opportunities to leverage new technologies and approaches to improve our applications and processes.

• Assess and mitigate technical risks.

• Support the implementation of DevOps and MLOps practices for seamless deployment and management of applications and models.

• Contribute to the development of the technical roadmap and strategy.

Required Qualifications:

• Bachelors degree in Computer Science, Engineering, or a related field; or equivalent practical experience.

• 14-18 years of experience in solution architecture, with a strong focus on application development.

• Proven experience in designing and implementing microservices architectures.

• Demonstrated experience with Generative AI concepts, models (e.g., LLMs), and their application in software solutions.

• Solid understanding of Machine Learning principles, workflows, and the integration of ML models into applications.

• Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.

• Proficiency in at least one modern programming language (e.g., Python, Java, Node.js, C#).

• Experience with web application development technologies and frameworks (frontend and/or backend).

• Strong understanding of database technologies (relational and/or NoSQL).

• Experience with API design and management.

• Familiarity with DevOps principles and CI/CD pipelines.

• Excellent communication, presentation, and interpersonal skills with the ability to explain complex technical concepts to both technical and non-technical audiences.

• Strong analytical and problem-solving skills.

Technical Skills:

• Architectural Patterns: Microservices, Event-Driven Architecture, API Gateway, etc.

• Cloud Platforms: AWS (EC2, S3, Lambda, Sagemaker, etc.), Azure (VMs, Blob Storage, Azure ML, etc.), GCP (Compute Engine, Cloud Storage, Vertex AI, etc.).

• Programming Languages: Python, Java, Node.js, C#, [any other relevant languages].

• AI/ML: Generative AI models (LLMs, Diffusion Models), Machine Learning algorithms, model training and inference, MLOps.

• Databases: SQL (e.g., PostgreSQL, MySQL), NoSQL (e.g., MongoDB, Cassandra).

• Web Technologies: [Specify relevant frontend/backend frameworks and technologies, e.g., React, Angular, Vue.js, Spring Boot, Node.js].

• DevOps & MLOps Tools: Git, Jenkins, GitHub Actions, GitLab CI, Docker, Kubernetes, [relevant ML lifecycle tools].

• API Technologies: REST, GraphQL.

• Other: Messaging queues (e.g., Kafka, RabbitMQ), Caching mechanisms, Monitoring and logging tools.